Study On Economy Of 300mw CFB Boiler Unit Peak-shaving Based On Large Data Application

How to ulilize peaking operation data accumulated by coal consumption real-time monitoring system and resolve the problem of inefficiency due to long-term underload operation of Yunnan Thermal Power Unit to make the unit more economic under current condition? The Key is to determine the present operating parameter.This paper focuses on underload operation data of a 300 MW CFB Unit of Yunnan Power Grid, discussed the method of data processing and principle. Then, in order to find the needed information hidden in large data, this paper introduced the properties of fuzzy clustering and multidimensional association rules algorithm to analyze the data.Through the analysis of the unit underload data by property fuzzy clustering and multidimensional association rules, when loading 105MW-135 MW, 135MW-165 MW, 165MW-195 MW and 195MW-225 MW, unit heat rate difference reach optimization respectively, we get the corresponding parameter operating rules. The results that property fuzzy clustering analysis can simplify and influence the constitution of the unit economic indicator parameter and multidimensional association rules can obtain the operating parameter reference values, indicate that data mining algorithm based on coal consumption online monitoring system is an effective way to improve efficiency of the unit and can provide more scientific guidance for Thermal Power Unit of Yunnan Power Grid.